Machine learning techniques available in pRoloc
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Using pRoloc for spatial proteomics data analysis
This tutorial illustrates the usage of the pRoloc R package for the analysis and interpretation of spatial proteomics data. It walks the reader through the creation of MSnSet instances, that hold the quantitative proteomics data and meta-data and introduces several aspects of data analysis, including data visu-alisation and application of machine learning to predict protein localisation.
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This tutorial illustrates the usage of the pRoloc R package for the analysis and interpretation of spatial proteomics data. It walks the reader through the creation of MSnSet instances, that hold the quantitative proteomics data and meta-data and introduces several aspects of data analysis, including data visualisation and application of machine learning to predict protein localisation.
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MOTIVATION Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools. RESULTS Here we present pRoloc, a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spati...
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